Search Results for author: Sven Koenig

Found 33 papers, 7 papers with code

Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

no code implementations4 Mar 2022 Jingkai Chen, Jiaoyang Li, Yijiang Huang, Caelan Garrett, Dawei Sun, Chuchu Fan, Andreas Hofmann, Caitlin Mueller, Sven Koenig, Brian C. Williams

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.

Motion Planning Multi-Agent Path Finding

Multi-Robot Routing with Time Windows: A Column Generation Approach

no code implementations16 Mar 2021 Naveed Haghani, Jiaoyang Li, Sven Koenig, Gautam Kunapuli, Claudio Contardo, Amelia Regan, Julian Yarkony

We formulate the problem as a weighted set packing problem where the elements in consideration are items on the warehouse floor that can be picked up and delivered within specified time windows.

Autonomous Vehicles

Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search

no code implementations12 Mar 2021 Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Sven Koenig

Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents.

Multi-Agent Path Finding

Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search

no code implementations10 Dec 2020 Taoan Huang, Bistra Dilkina, Sven Koenig

In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work.

Multi-Agent Path Finding

EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

1 code implementation3 Oct 2020 Jiaoyang Li, Wheeler Ruml, Sven Koenig

ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal.

Multi-Agent Path Finding online learning

Integer Programming for Multi-Robot Planning: A Column Generation Approach

no code implementations8 Jun 2020 Naveed Haghani, Jiaoyang Li, Sven Koenig, Gautam Kunapuli, Claudio Contardo, Julian Yarkony

We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints.

Embedding Directed Graphs in Potential Fields Using FastMap-D

1 code implementation4 Jun 2020 Sriram Gopalakrishnan, Liron Cohen, Sven Koenig, T. K. Satish Kumar

FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs.

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

1 code implementation15 May 2020 Jiaoyang Li, Andrew Tinka, Scott Kiesel, Joseph W. Durham, T. K. Satish Kumar, Sven Koenig

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions.

Multi-Agent Path Finding

Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

1 code implementation7 Mar 2020 Eric Heiden, Luigi Palmieri, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig

Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics.

Autonomous Driving Motion Planning

Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers

no code implementations30 Nov 2019 Ngai Meng Kou, Cheng Peng, Hang Ma, T. K. Satish Kumar, Sven Koenig

In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin.

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

1 code implementation19 Jun 2019 Roni Stern, Nathan Sturtevant, Ariel Felner, Sven Koenig, Hang Ma, Thayne Walker, Jiaoyang Li, Dor Atzmon, Liron Cohen, T. K. Satish Kumar, Eli Boyarski, Roman Bartak

The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other.

Autonomous Vehicles

Automatic Algorithm Selection In Multi-agent Pathfinding

no code implementations10 Jun 2019 Devon Sigurdson, Vadim Bulitko, Sven Koenig, Carlos Hernandez, William Yeoh

In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other.

Position Paper: From Multi-Agent Pathfinding to Pipe Routing

no code implementations21 May 2019 Gleb Belov, Liron Cohen, Maria Garcia de la Banda, Daniel Harabor, Sven Koenig, Xinrui Wei

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment.

Multi-Agent Path Finding Robot Navigation

Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery

no code implementations15 Dec 2018 Hang Ma, Wolfgang Hönig, T. K. Satish Kumar, Nora Ayanian, Sven Koenig

For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.

Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems

no code implementations30 Mar 2018 Hang Ma, Wolfgang Hönig, Liron Cohen, Tansel Uras, Hong Xu, T. K. Satish Kumar, Nora Ayanian, Sven Koenig

In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account.

Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments

no code implementations4 Oct 2017 Hang Ma, Jingxing Yang, Liron Cohen, T. K. Satish Kumar, Sven Koenig

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations.

Multi-Agent Path Finding

The FastMap Algorithm for Shortest Path Computations

no code implementations8 Jun 2017 Liron Cohen, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, T. K. Satish Kumar

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space.

Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

no code implementations8 Jun 2017 Liron Cohen, Glenn Wagner, T. K. Satish Kumar, Howie Choset, Sven Koenig

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics.

Multi-Agent Path Finding

Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

1 code implementation30 May 2017 Hang Ma, Jiaoyang Li, T. K. Satish Kumar, Sven Koenig

In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting.

Multi-Agent Path Finding

Path Planning with Kinematic Constraints for Robot Groups

no code implementations25 Apr 2017 Wolfgang Hönig, T. K. Satish Kumar, Liron Cohen, Hang Ma, Sven Koenig, Nora Ayanian

Path planning for multiple robots is well studied in the AI and robotics communities.

Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

no code implementations17 Feb 2017 Hang Ma, Sven Koenig, Nora Ayanian, Liron Cohen, Wolfgang Hoenig, T. K. Satish Kumar, Tansel Uras, Hong Xu, Craig Tovey, Guni Sharon

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research.

Multi-Agent Path Finding

Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program

no code implementations1 Feb 2017 Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio E. Cardona-Rivera, Tiago Machado, Tom Williams

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia).

Ethical Considerations in Artificial Intelligence Courses

no code implementations26 Jan 2017 Emanuelle Burton, Judy Goldsmith, Sven Koenig, Benjamin Kuipers, Nicholas Mattei, Toby Walsh

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses.

Optimal Target Assignment and Path Finding for Teams of Agents

no code implementations17 Dec 2016 Hang Ma, Sven Koenig

On the low level, CBM uses a min-cost max-flow algorithm on a time-expanded network to assign all agents in a single team to targets and plan their paths.

Multi-Agent Path Finding

Multi-Agent Path Finding with Delay Probabilities

no code implementations15 Dec 2016 Hang Ma, T. K. Satish Kumar, Sven Koenig

Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search.

Multi-Agent Path Finding

Theta*: Any-Angle Path Planning on Grids

1 code implementation16 Jan 2014 Kenny Daniel, Alex Nash, Sven Koenig, Ariel Felner

Angle-Propagation Theta* achieves a better worst-case complexity per vertex expansion than Basic Theta* by propagating angle ranges when it expands vertices, but is more complex, not as fast and finds slightly longer paths.

BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm

no code implementations15 Jan 2014 William Yeoh, Ariel Felner, Sven Koenig

Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems.

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